1. Business Intelligence

2. Topics Chart Online Analytical Process, OLAP – Excel’s Pivot table – Data visualization with dashboard Data warehousing Data Mining Scenario Management

3. Charting Decision Rules An Internet Service Provider charges customers based on hours used: – First 10 hours$15 – Each of the next 20 hours$2 per hour – Hours over 30 hours$1 per hour

4. Comparing Decision Rules Plan 2: – First 20 hours: $20 – Hours over 20$1.5 Plan 3: – $35 unlimited access.

5. Charting Functions Demand function: – P = 150 – 6*Q ^2 Supply function: – P = 10* Q ^2 + 2*Q Note: – Positive area – Value axis maximum/minimum value: Format Value Axis

6. Chart Stock Market Data Download Dow Jones Historical Data – Yahoo/Finance/Dow Jones/Historical Data To chart: – Insert/Chart/Other Charts/Stock chart

7. On-Line Analytical Processing (OLAP) Tools The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques OLAP Operations – Cube slicing–come up with 2-D view of data – Drill-down–going from summary to more detailed views – Roll-up – the opposite direction of drill-down – Reaggregation – rearrange the order of dimensions

8. Slicing a data cube

9. Example of drill-down Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells

10. Excel’s Pivot Table Insert/Pivot Table or Pivot Chart – Drill down, rollup and reaggregation – Filter Pivot Chart – Filter – Drilldown, rollup, reaggregation Import queries from Access to perform analysis. – Sales related to: Customer’s location, Rating and Products

11. Data Visualization Representing data in graphical/multimedia formats for analysis. – Web-based “dashboards” http://www.dundas.com/ – Dashboard Samples

12. Data Warehouse Data warehouse is a repository of an organization's electronically stored data. A data warehouse houses a standardized, consistent, clean and integrated form of data that: – sourced from various operational systems in use in the organization, – structured in a way to specifically address the reporting and analytic requirements.

13. Example: Transaction Database Customer Order Product Has 1 M M M CID Cname City OIDODate PID Pname Price Rating SalesPerson Qty

14. Analyze Sales Data Detailed Business Data Total sales: – by product: Qty*Price of each detail line Sum (Qty*Price) Detailed business data: qty*price Total quantity sold: – By product: Sum(Qty) Detailed business data: Qty

15. Dimensions for Data Analysis: Factors relevant to the business data Analyze sales by Product Analyze sales related to Customer: – Location: Sales by City – Customer type: Sales by Rating Analyze sales related to Time: – Quarterly, monthly, yearly Sales Analyze sales related to Employee: – Sales by SalesPerson

16. Data Warehouse Design - Star Schema - Dimension tables – contain descriptions about the subjects of the business such as customers, employees, locations, products, time periods, etc. Fact table – contain detailed business data with links to dimension tables.

17. Star Schema FactTable LocationCode PeriodCode Rating PID Qty Amount Location Dimension LocationCode State City CustomerRating Dimension Rating Description Product Dimension PID Pname Category Period Dimension PeriodCode Year Quarter Can group by State, City

18. Define Location Dimension Location: – In the transaction database: City – In the data warehouse we define Location to be State, City San Francisco -> California, San Francisco Los Angeles -> California, Los Angeles – Define Location Code: California, San Francisco -> L1 California, Los Angeles -> L2

19. Define Period Dimension Period: – In the transaction database: Odate – In the data warehouse we define Period to be: Year, Quarter Odate: 11/2/2003 -> 2003, 4 Odate: 2/28/2003 -> 2003, 1 – Define Period Code: 2003, 4 -> 20034 2003, 1 -> 20031

20. The ETL Process E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

21. The ETL Process Capture/Extract Transform – Scrub(data cleansing),derive – Example: City -> LocationCode, State, City OrderDate -> PeriodCode, Year, Quarter Load and Index ETL = Extract, transform, and load

22. Performing Analysis Analyze sales: – by Location – By Location and Customer Type – By Location and Period – By Period and Product Pivot Table: – Drill down, roll up, reaggregation

23. Data Mining Knowledge discovery using a blend of statistical, Artificial Intelligence, and computer graphics techniques Goals: – Explain observed events or conditions – Explore data for new or unexpected relationships

24. Typical Data Mining Techniques Statistical regression Decision tree induction Clustering – discover subgroups Affinity – discover things with strong mutual relationships Sequence association – discover cycles of evens and behaviors Rule discovery – search for patterns and correlations

25. Typical Data Mining Applications Profiling populations – High-value customers, credit risks, credit card fraud Analysis of business trends Target marketing Campaign effectiveness Product affinity – Identifying products that are purchased concurrently Up-selling – Identifying new products and services to sell to a customer based on critical events

26. Affinity Analysis: Market Basket Analysis Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Typically the relationship will be in the form of a rule: Example: – IF {beer, no bar meal} THEN {chips}.

27. Basket Analysis and Cross- Selling For instance, customers are very likely to purchase shampoo and conditioner together, so a retailer would not put both items on promotion at the same time. The promotion of one would likely drive sales of the other. A widely used example of cross selling on the internet with market basket analysis is Amazon.com's use of suggestions of the type: – "Customers who bought book A also bought book B", e.g.

28. Scenario A scenario is an assumption about input variables. Excel’s Scenarios is a what-if-analysis tool. A scenario is a set of values that Microsoft Excel saves and can substitute automatically in your worksheet. You can use scenarios to forecast the outcome of a worksheet model. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results. Data/What If analysis/Scenario

29. Creating a Scenario – Add scenario Changing cells – Scenario Summary Resulting cells Demo: benefit782.xls